States.
The Halting Policy Network's first job is to compute a probability of halting \(p_{t^\prime }\)  given the environment's state at time \(t^\prime \)  which can then be used to take an action: Stop now, or Wait for more data to be collected.
While prior methods use only the prefix embedding \(h_{t^\prime }\)  to represent the ongoing system's current state [1]}, we propose also predicting an intermediate classification \(y_{t^\prime }\)  via the Prefix Classifier for each hidden state to gauge the model's current opinion of the classes. By feeding this information to the Halting Policy Network, it can learn the relationship between the Prefix Classifier's confidence and the likely accuracy, which often depends on the task.
Additionally, we input \(t^\prime \)  as additional knowledge of a prediction's earliness.
